However, due to the non-linear relationship between Rs and temperature, in annual time-scale, soil respiration at mean temperature cannot directly represent annual soil respiration.
Even though, Bahn et al. (2010) found that Rs measured at mean temperature have a clear relationship with Rs_annual, based on 80 sites across global, Bahn et al. developed a exponential model to predict Rs_annual through Rs_mat (no drought stress sites: Rs_annual = 455.8 Rs_mast^1.0054, with drought stress sites:Rs_annual = 436.2 Rs_mast^0.926).
Citation: Bahn’s approach Bahn et al. (2004) Biogeosciences
Rs measured at mean annual soil temperature
Data
Rs_AnnualStatistics
Rs_mat based on the annual mean soil temperature, T_Annual, and/or MATRs_annual based on Rs_matRs_annual and Rs_annual_bahn to evaluate the performance of Bahn model across the globalUpdate Bahn’s model
We thus exhaustively examed the possibilities cause the Rs_annual_bahn vs Rs_annual not following 1:1 line.
At first, we tested the soil temperature sources and its effect on the Rs_annual_bahn vs Rs_annual. The results show that Ts sources do not have clear effects on the Rs_annual_bahn and Rs_annual relationship.
From MGRsD means mean annual soil temperature (amst) are from a global monthly soil respiration database, each site has more than 12 months measured soil temperature read from original papers.
From paper means amst were reported from the original paper (table, figures, or description).
Partly from TAIR means: some studies did not measure soil temperature all year aroud, for those months, we predic soil temperature based on monthly air temperature (Tsoil = 2.918 + 0.829*Tair, this model was developed based on the sites which have >= 12 months soil temperature measurements).
Rs_Ts_relationship: there are 67 records I cannot get the soil temperature information through above three methods. Based on the Rs_Ts_relationship and reported Rs_annual, I calculated the amst.
The calculated amst was then compared with the annual Tair, if they are well matched (error < 5%), calculated mast were used.
Calculated amst and annual Tair not well match usually indicate a potential problem, then I go back to the manuscript and check out what is the problem.
Whenever a paper reported annual mean Ts, I compared the reported mast and estimated mast based on the Rs_Ts_relationship, I found they are well matched.
RA dominated sites tend to have larger intercept than RH dominated sites, but no difference in slope.
We tested Q10 and R10 at Ra and Rh dominate sites, Ra dominate sites have larger Q10 (0-10, 5-10, 10-20, and 0-20) vaues and R10 values.
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The red dots are Rs measured from Mediterranean
The blue dots are Rs measured from other biomes
Red and blue solid lines are new1 models for Mediterranean and exclude-Mediterranean, respectively
Red and blue dashed lines are Bhan (2010) models for Mediterranean and exclude-Mediterranean, respectively
We have much more measurements Rs in mid-latitude regions, where developed countries are mostly located
It is difficult to measure soil respiration all year around in cold regions, but critical because of high rates of climate change and large soil C stocks
Less-developed countries are constrained by lack of resources, and thus we do not have enough measurements from spouth hetmesphere, arctic, and tropical regions (Xu and Shang 2016)
Global spatial distribution of soil respiration sites
Using this approach to estimate global Rs, and Rs trend? see how it differ from traditional approach (Rs~Ts relationship).
But, in order to predict global Rs using this approach, we need a uniform model to estimate Rs_amat (because for most sites, we do not have a site-scale-specific Rs~temp relationship).
We can also using random forest model, in this case, we do not need a relationship to calculate Rs_amat.
Using Rs_amat (or Rs_mst) predict Rh_annual?
1 Using SD information with boosting?
2 Think about application
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